Overview
The fixest
package is a powerful and versatile tool for analysing panel data in R. It is fast, memoryefficient, and offers a wide range of options for controlling the estimation process. Its main strength is the ability to estimate fixed effects models, which are commonly used in panel data analysis to control for unobserved heterogeneity at the individual or group level. Compared to others, the main advantage of the fixest
package is computational prowess, which lets it estimate models in a fraction of the time compared to many other packages. This is especially consequential when your model entails highly dimensional fixed effects, i.e., fixed effects with many different levels, such as a fixed effect for several thousand stores in your sample. The â€śtrickâ€ť fixest
uses to do this is described in great detail in BergnĂ© (2018). In addition to fixed effects models, the fixest
package includes functions for estimating other (non)linear models such as Poisson models, and negative binomial models.
Using a stylised example, we illustrate how you can use various functions in the fixest
package for fixedeffect estimation. For illustration purposes, we will use the Grunfeld dataset which contains investment data for 11 U.S. Firms. It contains information on the following variables:
Variables  Description 

invest 
Gross investment in 1947 dollars 
value 
Market value as of Dec 31 in 1947 dollars 
capital 
Stock of plant and equipment in 1947 dollars 
firm 
 General Motors  US Steel  General Electric  Chrysler  Atlantic Refining  IBM  Union Oil  Westinghouse  Goodyear  Diamond Match  American Steel 
year 
19351954 
Load packages
# Load packages
library(fixest)
library(AER)
## load Grunfeld data from the AER package
data(Grunfeld)
Estimation with feols()
We can estimate a fixedeffects model using the feols()
function, which can be used to estimate linear fixedeffects models. The estimation equation in this example is as follows:
where,
 $invest_{it}$ is the gross investment of firm
i
in yeart
 $value_{it}$ is the market value of assets of firm
i
in yeart
 $capital_{it}$ is the stock value of plant and equipment of firm
i
in yeart
 $\alpha_i$ is the fixed effect for firm
i
(capturing unobserved firmspecific factors that don’t vary over time)  $\delta_t$ is the fixed effect for year
t
(capturing unobserved yearspecific factors that are common to all firms in that year)  $\epsilon_{it}$ is the error term, which includes all other unobserved factors that affect investment but are not accounted for by the independent variables or the fixed effects.
Standard Errors
When dealing with panel data, one must expect biased standard errors. An excellent paper on this issue and a good starting point for deciding how this may affect your research is Petersen (2009). Most commonly, the bias results from either correlation between different time periods of a group (a group effect) or a correlation across different groups within the same time period (a time effect). While the former is often called serial correlation, the latter is called crosssectional dependence. For both, clustering your standard errors can be a remedy. As a free â€śbonus,â€ť clustering your standard errors also helps you to address heteroskedasticity, the fact that the error term is not distributed equally across your observations.
Oneway clustering is used when clustering is along one dimension only, and twoway clustering is used when clustering is along two dimensions. Usually, your clusters are either the group, the time, or both. While oneway and twoway clustering is the most commonly used ways of clustering, the number of potential clusters is not limited to two. For example, if you are a large national retailer and want to analyze customer purchases in your stores over several months, you could easily justify clustering on (1) the individual store (the group), the individual month (the time), and (3) the store’s city. By clustering your standard errors on the city, you acknowledge that some unobserved regional factors may influence stores in the same city equally. When deciding on your approach to clustering, you should also remember that clustering is only sensible when the number of clusters you create is sufficiently high. As usual with statistics, what is sufficient is subject to debate, but frequently a number between 3050 comes up. Following that argument, if you have stores from less than 30 cities in your data, you should abstain from clustering on the city level. In addition, when you only have one store in each city, clustering on the city becomes equal to clustering on the store.
After deciding on the right way to cluster, we can cluster standard errors using the cluster
option in feols()
for oneway or twoway clustering.
# oneway cluster by firm
feols_model< feols(invest ~ value + capital  firm + year , data = Grunfeld, cluster = ~firm)
# twoway clustering by firm and year
feols_model< feols(invest ~ value + capital  firm + year , data = Grunfeld, cluster = ~firm + year)
Additionally, the vcov()
function in the fixest
package is a handy way to easily adjust standard errors. It returns the estimated variancecovariance matrix of the model parameters. It contains the se
option which can be used to specify the type of standard errors to compute. The following standard error types can be specified:

standard
: This is the default option when no clustering is used. It computes the standard erors assuming independent and identically distributed errors. This is appropriate when the errors are uncorrelated. 
hetero
: This option computes heteroskedasticityrobust standard errors. It assumed that the errors are uncorrelated but may have different variances. This is appropriate when the varianve of the errors varies across observations. 
cluster
: This option computes clusterrobust standard errors. It accounts for correlation of errors within clusters. This is appropriate when there are groups of observations that are likely to be correlated with each other. 
twoway
: This option computes twoway clusterrobust standard errors. It accounts for correlation of errors within two clusters, which can be useful when there are multiple sources of correlation in the data. Similarly, you can also compute threeway and fourway cluster robust standard errors using the optionsthreeway
andfourway
respectively.
# estimate linear twoway fixed effect model with twoway clusting
feols_model< feols(invest ~ value + capital  firm + year , data = Grunfeld, cluster = ~firm + year)
# get variancecovariance matrix with heteroskedasticity robust standard errors
hetero = vcov(feols_model, se = "hetero")
Export results
We can print a summary of the model using the summary
function. Alternatively, one could also use the etable
function and export the results to Latex by passing an additional argument: tex = TRUE
.
Importantly, we can explicitly specify the variancecovariance matrix to be used. Continuing with our example, we had computed heteroskedasticityrobuststandard errors with twoway clustering. We can include this using the .vcov
option in the summary()
function and the se
option under the etable()
function.
summary(feols_model, .vcov = hetero) # hetero is the varcov matrix that was previously computed using the vcov function
# OR
etable(feols_model, se = "white")
Extract the fixedeffect coefficients
Use the fixef()
function to obtain the fixedeffects of the estimation. The summary()
method helps to get a quick overview:
Note that the mean values are meaningless per se, but give a reference point to which to compare the fixedeffects within a dimension
Finally, the plot
function helps to distinguish the most notable fixedeffects with the highest and lowest values for each of the fixedeffect dimensions.
Other Estimation Methods in fixest
Aside from feols()
, here are additional functions useful for estimation based on the model requirements:
Function  Description  Use 

feglm 
Generalized linear models  This function estimates fixedeffects generalized linear models, which can handle nonlinear outcome variables and nonnormal error distributions. 
femlm 
Maximum likelihood estimation  This function estimates fixedeffects maximum likelihood models, which can handle a wide range of distributions and can include both continuous and categorical variables. 
feNmlm 
Nonlinear in RHS parameters  This function estimates fixedeffects nonlinear models where the nonlinearity is in the righthand side parameters. 
fepois 
Poisson fixedeffect  This function estimates fixedeffects Poisson models, which are commonly used for count data that follow a Poisson distribution. 
fenegbin 
Negative binomial fixedeffect  This function estimates fixedeffects negative binomial models, which are commonly used for count data that exhibit over dispersion relative to the Poisson distribution. 
Here are the key takeaways from this building block:
 Use the
feols()
function to estimate linear fixedeffect models with clustered standard errors using thecluster
option.  Use the
vcov()
function to easily adjust and compute robust standard errors.  Export the estimation results using the
summary()
oretable()
function and specify the standard errors using.vcov
andse
options respectively.  Extract the fixedeffect coefficients using the
fixef()
function.  Other estimation methods you can use in
fixest
.